Data Science with Semantic Technologies -

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Preface xv

1 A Brief Introduction and Importance of Data Science 1
Karthika N., Sheela J. and Janet B.

1.1 What is Data Science? What Does a Data Scientist Do? 2

1.2 Why Data Science is in Demand? 2

1.3 History of Data Science 4

1.4 How Does Data Science Differ from Business Intelligence? 9

1.5 Data Science Life Cycle 11

1.6 Data Science Components 13

1.7 Why Data Science is Important 14

1.8 Current Challenges 15

1.8.1 Coordination, Collaboration, and Communication 16

1.8.2 Building Data Analytics Teams 16

1.8.3 Stakeholders vs Analytics 17

1.8.4 Driving with Data 17

1.9 Tools Used for Data Science 19

1.10 Benefits and Applications of Data Science 28

1.11 Conclusion 28

References 29

2 Exploration of Tools for Data Science 31
Qasem Abu Al-Haija

2.1 Introduction 32

2.2 Top Ten Tools for Data Science 35

2.3 Python for Data Science 35

2.3.1 Python Datatypes 36

2.3.2 Helpful Rules for Python Programming 37

2.3.3 Jupyter Notebook for IPython 37

2.3.4 Your First Python Program 38

2.4 R Language for Data Science 39

2.4.1 R Datatypes 39

2.4.2 Your First R Program 41

2.5 SQL for Data Science 44

2.6 Microsoft Excel for Data Science 48

2.6.1 Detection of Outliers in Data Sets Using Microsoft Excel 48

2.6.2 Regression Analysis in Excel Using Microsoft Excel 50

2.7 D3.JS for Data Science 57

2.8 Other Important Tools for Data Science 58

2.8.1 Apache Spark Ecosystem 58

2.8.2 MongoDB Data Store System 60

2.8.3 MATLAB Computing System 62

2.8.4 Neo4j for Graphical Database 63

2.8.5 VMWare Platform for Virtualization 65

2.9 Conclusion 66

References 68

3 Data Modeling as Emerging Problems of Data Science 71
Mahyuddin K. M. Nasution and Marischa Elveny

3.1 Introduction 72

3.2 Data 72

3.2.1 Unstructured Data 74

3.2.2 Semistructured Data 74

3.2.3 Structured Data 76

3.2.4 Hybrid (Un/Semi)-Structured Data 77

3.2.5 Big Data 78

3.3 Data Model Design 79

3.4 Data Modeling 81

3.4.1 Records-Based Data Model 81

3.4.2 Non-Record-Based Data Model 84

3.5 Polyglot Persistence Environment 87

References 88

4 Data Management as Emerging Problems of Data Science 91
Mahyuddin K. M. Nasution and Rahmad Syah

4.1 Introduction 92

4.2 Perspective and Context 92

4.2.1 Life Cycle 93

4.2.2 Use 95

4.3 Data Distribution 98

4.4 CAP Theorem 100

4.5 Polyglot Persistence 101

References 102

5 Role of Data Science in Healthcare 105
Anidha Arulanandham, A. Suresh and Senthil Kumar R.

5.1 Predictive Modeling-Disease Diagnosis and Prognosis 106

5.1.1 Supervised Machine Learning Models 107

5.1.2 Clustering Models 110

5.1.2.1 Centroid-Based Clustering Models 110

5.1.2.2 Expectation Maximization (EM) Algorithm 110

5.1.2.3 DBSCAN 111

5.1.3 Feature Engineering 111

5.2 Preventive Medicine-Genetics/Molecular Sequencing 111

5.2.1 Technologies for Sequencing 113

5.2.2 Sequence Data Analysis with BioPython 114

5.2.2.1 Sequence Data Formats 114

5.2.2.2 BioPython 117

5.3 Personalized Medicine 121

5.4 Signature Biomarkers Discovery from High Throughput Data 122

5.4.1 Methodology I - Novel Feature Selection Method with Improved Mutual Information and Fisher Score 123

5.4.1.1 Algorithm for the Novel Feature Selection Method with Improved Mutual Information a...